Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Round 1

Reviewer 1 Report

Dear Authors

 

Initially, I congratulated the authors for the proposed research. The theme is a widely researched topic with several innovations. The present work presents a bias on the traditional perspective. However, when analyzing the work, we observed several opportunities for improvement of the work, as described below:

 

1. In the introduction, the authors should better highlight the research questions more precisely. The problem and objectives are not transparent or expressed in the introduction.

2. I suggest adding a paragraph at the end of the introduction summarizing the structure of the paper.

3. The methodology and the results were not referenced in the introduction.

4. I suggest modifying the title of section 2 for the literature review.

5. I suggest expanding the literature review by including non-traditional methods such as multicriteria and machine learning methods that have been used in the study of stock portfolios, as indicated below:

5.1 Juškaitė, L., & Gudelytė-Žilinskienė, L. (2022). Investigation of the feasibility of including different cryptocurrencies in the investment portfolio for its diversification. Business, Management and Economics Engineering, 20(1), 172–188. https://doi.org/10.3846/bmee.2022.16883

5.2 Aljinović, Z.; Marasović, B.; Šestanović, T. Cryptocurrency Portfolio Selection—A Multicriteria Approach. Mathematics 2021, 9, 1677. https://doi.org/10.3390/math9141677

5.3 Basilio, M.P., de Freitas, J.G., Kämpffe, M.G.F. and Bordeaux Rego, R. (2018), "Investment portfolio formation via multicriteria decision aid: a Brazilian stock market study", Journal of Modeling in Management, Vol. 13 No. 2, pp. 394-417. https://doi.org./10.1108/JM2-02-2017-0021

5.4 Enhancement of equity portfolio performance using data envelopment analysis. https://doi.org/10.1016/j.ejor.2012.02.006

5.5 Stock selection multicriteria decision-making method based on elimination and choice translating reality I with Z-numbers. https://doi.org/10.1002/int.22556

 

6. The title of section 3 may be replaced by “background on the measurement of portfolio risk”;

 

7. The authors did not clearly describe the methodology, I suggest reorganizing the text structure;

 

8. Expand the conclusion by exposing the advantages and disadvantages of the proposed model of the traditional ones.

9. The graphics are well illustrated.

 

Best Regards

 

Reviewer.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors consider an analysis of asset returns and risk. It is a valuable analysis. They consider especially deviations from exact Gaussian distributions and consider cryptocurrencies.

They give explicit results. Especially they found that cryptocurrencies are (not surprisingly) more volatile.

Everything is well described. The results are new and suitable for a publication in Risk and Financial Management.

However, some shortcomings and limitations must be discussed. It is probably easies to include it in the Conclusions:

 i) Asset prices such as stock prices do not fluctuate randomly around their "true" value. Because stock prices and especially cryptocurrencies are not conserved values, there is no "true" value.
ii) Even assuming an equilibrium price, this equilibrium is by no means stable.

iii) Even if a price fluctuates around an equilibrium, these fluctuations are not random. They are chaotic. This may lead to a distribution which looks similar to a Gaussian. But it is statistically completely different.

iv) Even if one has a Gaussian like distribution, the standard deviation is not necessarily equal to sigma. This is because the distributions here do never run from minus infinity to plus infinity.

v) Drawing conclusions from distributions which are not exactly Gaussian need a different approach. As stated by the authors the distributions are not completely Gaussian here.

Some more details can be found in the reviewed manuscript which is included.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Dear Authors

 

After reviewing the revised paper, I found that the authors have implemented the suggestions indicated by the reviewers. Thus, I do not see any other improvements to be made in this version.

I congratulate you on the proposed model.

I congratulate you on the proposed model.

Best Regards